COMM1110 Evidence-Based Problem Solving
Due date: Week 5: 11.59am, Friday 15th March
________________________________________________________________
You are a consultant at Solution-X, a leading consulting firm well-regarded for its innovative
and comprehensive problem-solving solutions that blend analytical scrutiny, statistical
insights, and ethical considerations.
We've partnered with GreenMart, a retail supermarket chain facing a pressing issue: a
notable increase in food waste, especially in the fresh food (Fruit and Vegetable)
section. GreenMart is eager to delve into the root causes of this waste surge and
seeks actionable recommendations to tackle the problem head-on.
GreenMart's fresh food section, brimming with fruits, veggies, dairy, meat, seafood,
and bakery delights. Yet, a rising trend in food waste threatens to dampen the
excitement. Whether it's items nearing expiry, facing damage, falling short of quality
standards, or spoiling due to storage mishaps, the waste is both a financial setback
and an environmental headache.
Now, GreenMart isn't just about profits; they're deeply committed to operational
efficiency, reducing their environmental footprint, and championing sustainability.

1. Investigate the factors contributing to the rise in food waste in GreenMart’s fresh
food section.
2. Develop and present well-considered and actionable recommendations to
GreenMart to assist them in implementing effective strategies to reduce food
waste.
2
Your Role and Responsibilities
Solution-X has formed a team of consultants and business analysts to provide thorough
analysis and actionable recommendations to GreenMart. At the project's end, we'll compile a
comprehensive report. Your role, assigned by the team lead, is crucial to crafting practical
solutions. Let's get started on making a insightful business analysis report.
Your Tasks:
Assessment 3: Preliminary Analysis Pack (25%):
▪ Prepare a preliminary business analysis report for an internal meeting on the rise in food
waste at GreenMart's fresh food section. Use analytical, statistical, and ethical tools to
understand the drivers behind this increase. This analysis will guide discussions and shape
future solutions.
▪ Focus Areas:
− Analytical Toolbox: Identify potential factors and drivers contributing to the food
waste problem.
− Statistical Toolbox: Examine the data provided by GreenMart to identify food waste
trends and patterns. This analysis will aid in pinpointing the potential causes of the
issues, directing more detailed analysis, and focusing solution development on key
issues to prioritise your problem-solving effort.
− Ethics Toolbox: Identify any ethical dilemmas associated with food waste
• Word limit: 1,500 words (excluding graphs, figures, and reference list). An additional 10% buffer
(1,500 + 150 words) will be applied if you exceed the word count.
• Structure and Format: An introduction or executive summary is NOT required. You should
structure your responses to directly answer each question in the Initial Analysis Pack. Write in a
business report style, utilizing formal language, clear headings, and subheadings to organize your
responses to each question. The clarity, coherence, and organization of your report are crucial,
and each section should be well-integrated to offer comprehensive insights into the addressed
questions.
• Referencing Style: Please use the Harvard referencing style for any sources cited in your report
(see The 'In-Text' or Harvard method for more information).
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Guidelines for your Business Report
Section 1: Scoping the Problem Using the Analytical Toolbox (40%):
This section is approximately 600 words (guide only, not a word limit).
1) Define the Problem: Define the problem concisely to provide clarity on the main issue
requiring resolution for this assignment.
2) Scope the Problem: Utilize the 5Ws framework (What, Where, When, Who, Why) to frame
and scope the problem. For each 'W', formulate questions to explore various dimensions
of the problem and identify evidence required to substantiate the answers. Choose only
two 'Ws' for your report.
Instruction: Use the table below to organise your questions, evidence, and types of
evidence. You are required to provide at least three points for two ‘W’s (you can choose
any 2 “W” s from the 5 W).
Please integrate the 5W table provided directly into your report and type out your response
as text. All content within the table will count towards the 1,500-word limit, so avoid using
screenshots
2ws Questions to Explore the Problem Identified Evidence Type of Evidence
W.. 1.
2.
3.
1.
2.
3.
1.
2.
3.
W.. 1.
2.
3.
1.
2.
3.
1.
2.
3.

3) Break Down the Problem Using a Logic Tree: Construct a logic tree to systematically
analyse the increase in food waste at GreenMart, dividing the problem into its parts and
sub-parts to pinpoint specific areas of concern and contributing drivers.
Instruction:
a) Include a clearly labelled logic tree in your submission. The tree needs to meet the
Mutually Exclusive, Collectively Exhaustive (MECE) Requirements.
Instructions for creating a clear logic tree using PowerPoint are available on our
course Moodle page (Week 2's folder). Ensure all details in your logic tree are
clearly visible. Marks may be deducted if your tutor cannot read the details due to
blurriness. Attach your logic tree as an image to your report. The logic tree image
will NOT count towards the 1,500-word limit.
b) Prioritisation: Determine and justify which branches and/or sub-branches should
be prioritized for further analysis. Ensure coherence between the logic tree and
provide explanation to detail your analytical process, providing a clear rationale for
your choices.
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Section 2: Gaining Insights Using the Statistical Toolbox (40%):
This section is approximately 600 words (guide only, not a word limit).
Context:
After presenting your logic tree and having a detailed discussion with GreenMart, it has
been identified that a substantial portion of the increase in food waste is due to an
abundance of freshly packed fruits and vegetables reaching their expiration dates before
being sold. This insight has refined the focus of your investigation, necessitating a more
targeted analysis to comprehend the waste generated from these specific items.
GreenMart has shared a dataset with you, concentrating on these two food items. A
detailed description of the dataset is provided on page 6.
Order Details: Each record in the Excel dataset represents a single order, comprising 150
pre-packed fruits and vegetable items. The dataset provides insights into the quantities
wasted due to items remaining unsold before their expiration date
GreenMart Food Waste Allowance Target: GreenMart’s food waste allowance percentage for
fruit is capped at 6.67%, implying that in any given order of 150 pre-packed fruit items, a
maximum of 10 items should be wasted due to reaching expiration before being sold. The
waste percentage for vegetable is capped at 12% (a maximum of 18 Items per order).
GreenMart's Operational Protocols: When orders arrive at the GreenMart retail store, they are
initially placed in the storage area before being stocked on the shelves for sale. Operational
protocols with specific targets for shelving fresh food items are implemented to optimize the
availability of fresh products to customers while minimizing waste due to expiration.
− For fruit: Target is to have the items on the shelf for at least 7 days before the expiry
date, given an expiration date of 12 days post-arrival at the storage area.
− For vegetable: Target is to have the items on the shelf for at least 5 days before the
expiry date, given an expiration date of 8 days post-arrival at the storage area.
Statistical Toolbox Analysis Instructions:
1) Analyse Food Waste:
a) Summary Statistics: Calculate the mean, and standard deviation of food waste
(variable "Quantity_Wasted") for fruits and vegetables. Then, compare these
results with GreenMart’s food waste allowance target
b) Monthly Analysis: Create pivot tables in Excel to conduct a monthly analysis
of fruits and vegetables waste, using the variable "Order_Arrival_Date" to
determine the order month. Choose an appropriate visual representation you
see fit to showcase this result in your report.
2) Investigate Logistic Issues and Shelf Time
Management at GreenMart suspects that logistic issues may be causing delays in
moving items from storage to the shelves. This delay could reduce the display time of
food items, contributing to increased food waste due to items reaching their expiration
dates before being sold.
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a) Create a new variable: Create a new column named "Shelf_Duration" to
calculate the shelf time (in days) of each order. This variable represents the
duration each order stays on the shelf before expiration
Shelf_Duration = Expiration_Date_Of_Order – Inventory_Replenishment_Date
b) Summary Statistics and Monthly Analysis: Calculate the summary statistics
of “Shelf_Duration” for fruits and vegetables separately. Then, perform a
monthly analysis and choose a suitable diagram to present it in your report.
3) Analyse Number of Orders, Prices, and Additional Insights
a) Order and Price Analysis: Select suitable statistical analysis tools and
visualizations (diagrams) to examine the trends in the number of orders and
prices for fruits and vegetables over time.
b) Create another new column: Now, you're required to create a new column in
your Excel file. This column can contain any information you consider relevant,
ensuring that it's solely based on your existing Excel data. Clearly explain the
rationale behind creating this new column and how it can help you address the
food waste issue. Furthermore, include a screenshot displaying only the first
10-15 rows of this new column. Insert the screenshot into your report
alongside your explanation for this question. The screenshot does not count
towards the word count.
Instruction: Include clear tables or graphs to display the results of each statistical analysis
part (1-3). Ensure they are well-labelled and easy to read. Provide a summary of the main
findings from your analysis above, highlighting key insights obtained. Emphasize their
relevance to helping you solve the food waste issue. (Note: Tables, Diagrams, or Graphs
in this section are NOT in the word count)
Section 3: Ethical Dilemmas with the Ethics Toolbox (20%)
This section is approximately 300 words (guide only, not a word limit).
Select a stakeholder impacted by GreenMart's food waste issue. Stakeholders may include
GreenMart, residents and consumers, the Local council, or Regulatory Agencies like ACCC,
waste management companies, or product manufacturers.
Consider one ethical dilemma faced by your chosen stakeholder due to the increase in
food waste. Reflect on the various ethical concerns and challenges related to food waste
at GreenMart. Assess the potential harm to individuals or entities, such as the
environment, and elaborate on your reasoning (Please refer to our tutorial materials from
Week 4 for additional details and support).
Instruction: Ensure clarity and conciseness in your explanation, focusing on the ethical
implications and considerations of the identified dilemma within the context of
GreenMart's food waste issue.
For this section, you are NOT required to apply the full 7-step Ethical Decision-making
Framework; your task is merely to identify one potential ethical dilemma related to the
food waste issue at GreenMart, considering the perspective of your chosen stakeholder.
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You can access and download your personalised dataset for Assessment 3 through
the COMM1110 R-Shiny website using the following link:
Click this link to download your data - https://comm1110.shinyapps.io/comm1110/


Steps to Download Your Personal Excel Data

1. Open the provided link above and then click the "Project Data" button.
2. Enter your student ID (without the "z") and click "Load Project Data" to access
your personalized dataset.
3. Once your data loads, download it by clicking "Download Data" (Note: It will be
in CSV format).
4. Open the downloaded CSV file and save it as an "Excel Workbook (.xlsx)" before
conducting any analysis. This ensures that your work can be properly saved.

Important Notes
• The COMM1110 R-Shiny website is only used for downloading data set.
• Each student is provided with a personalized Excel file containing 500 orders.
• Follow the provided steps diligently to download your personalized Excel file.
Then, apply the Excel skills you learnt from tutorials and online weekly Excel
questions to analyze the dataset contained within your downloaded Excel file.
• Numeric Variable Errors: If the R-Shiny App displays errors related to non-numeric
variables, please ignore these error messages. Simply download your Excel data
file.
• If you have any issues with downloading your personal Excel file from the above
link, please contact our Course email at COMM1110@unsw.edu.au

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Dataset Overview:
Each student will receive a personalised dataset consisting of 500 records, collected over the
span of the 1/01/2023 to 31/12/2023. Each observation in the dataset represents detailed
information about individual food orders at GreenMart.
Variables:
The dataset encompasses 8 variables, each providing different insights into the food waste
issue at GreenMart. Here is a brief overview of each variable included in the dataset:
Variable Name Description Example
Values
Order_ID A unique identifier for each order. 43E4X6VIY
Order_Type The type of food item in the order. Fruit or
Vegetable
Price The Selling Price at which GreenMart sells
the item (Fruit and Vegetable) to their
consumers.
$28.46
Order_Arrival_Date The date the order arrives at the GreenMart
store (storage area).
*Note: Multiple orders can arrive at the
same date.
2/11/2023
Inventory_Replenishment_Date The date the order is moved to the shelf for
sale.
4/12/2023
Expiration_Date_Of_Order The expiration date of the food items in the
order.
11/01/2023
Quantity_Ordered The total quantity of food items ordered in
each order.
150
Quantity_Wasted The total quantity of pre-packed food items
wasted in this order due to not being sold
before expiration.
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PLANNING ASSISTANCE (Use of AI tool such as ChatGPT)
You may use AI software for initial idea generation, but your final submission must be
substantially your own work. Only occasional AI-generated words or phrases are allowed.
Keep copies of initial prompts for verification. Submission of AI-generated content will be
considered academic misconduct and subject to penalties.
Resources:
▪ Get individual feedback on you draft: https://www.student.unsw.edu.au/feedback-hub
▪ Study support (academic skills, English, etc.): https://www.student.unsw.edu.au/study-
support-and-education-support-advisors

Criteria 3 Fail

Pass

Credit

Distinction

High Distinction

1. Analytical
Problem-Solving
40% Does not define or scope the
problem accurately. The
logic tree is missing or
inaccurately constructed,
showing a lack of
understanding of the
problem.
Defines and scopes the
problem with minor
errors or omissions.
The logic tree is
present but may lack
full MECE compliance.
Clearly defines and
accurately scopes the
problem using the 5Ws
framework. Constructs a
coherent logic tree that
is largely MECE
compliant.
Clearly defines, accurately
scopes the problem, and
constructs a fully MECE
compliant logic tree.
Provides clear insights
derived from the logic tree.
Defines, scopes the problem
meticulously, and
constructs a sophisticated
logic tree that is fully MECE
compliant. Derives nuanced
insights and prioritises
branches effectively with
sound justification.
2. Statistical
Problem-Solving
40% Does not apply or
inaccurately applies
statistical tools. Visual
representation is unclear or
inappropriate, and insights
are missing or irrelevant.
Applies statistical tools
with minor errors or
omissions. Visual
representation is clear,
but insights may be
superficial.
Accurately applies
statistical tools, uses
appropriate visual
representation, and
generates relevant
insights.
Accurately and insightfully
applies statistical tools,
uses sophisticated visual
representation, and
generates deep, relevant
insights.
Accurately and insightfully
applies statistical tools,
uses innovative visual
representation, and
generates novel, profound
insights, demonstrating a
deep understanding of the
data and its implications.
3. Ethical Dilemma
Identification
20% Provides a partial or limited
description of an ethical
dilemma, which may not
constitute a real dilemma or
the links with ethics are
unclear.
Provides an adequate
description of a
generally appropriate
ethical dilemma with
some focus on relevant
details and
stakeholders.
Provides a sound
description of an
appropriate and well-
specified ethical
dilemma with solid
focus on relevant details
and stakeholders.
Provides clear and succinct
descriptions of an
appropriate and well-
specified ethical dilemma
with clear focus on relevant
details and stakeholders.
Provides a clear, succinct,
and compelling description
of a clearly specified and
appropriate ethical dilemma
with a very clear focus on
relevant details,
stakeholders, and the
ethical implications inherent
to the identified dilemma.